Writing

Pore Lab: Seeing Inside Reservoir Sandstone

From an X Post to a 3D Pore-Scale Simulator in an Hour

Adding a Geological Facies Display to a Well Log Plot in Matplotlib and Plotly

Adding a column of colour to a well log plot using Python

6 Ways to Customise Your Raincloud Plot

Splitting, highlighting, connecting means, tuning the cloud, styling the rain, and knowing when to go vertical

PetroPlots - A New Python Plotting Library for Well Log Data

When a Line Chart Becomes Spaghetti

Past a handful of series, a line chart stops letting you follow any of them. Here is what breaks and what to plot instead

Why a Histogram Can Show You the Wrong Shape

How the number and position of bins can change what you think the distribution is telling you

9 Scientific Data Plots and When They Fail

Every chart has a blind spot. Learn where common scientific visualisations start to hide the patterns that matter.

What Your Scatter Plot Stops Showing You Once It Gets Crowded

Overplotting is easy to miss and easy to fix, once you stop asking your chart to show every point.

Handling Categorical Geological Data in Machine Learning Models

What label, ordinal, and one hot encoding each tell a model about your rocks

From Default to Publication-Ready: The Raincloud Plot

Six steps between the chart ptitprince gives you and one you would want to put in a report

Raincloud Plots with PtitPrince: See What Your Data Is Really Doing

Three views of your data distribution in one function call

From Boxplot to Raincloud: Building a Better Distribution Plot in Python

Constructing a raincloud plot one layer at a time

How to Visualise Missing Data with an Interactive Plotly Heatmap

Explore well log data completeness with interactive heatmaps

Why Your Jupyter Notebooks Are Harder to Read Than They Should Be

Working code is only half the job. The reasoning needs to be readable too.

Six Matplotlib Changes That Improve Your Figures for Reporting and Publication

Small adjustments. Large visual difference.

AI Verification Depends on the Stakes

3 Jupyter Notebook Habits That Hold Up When Someone Else Opens Them

Small habits that make notebooks easier to rerun and share

Boxplots Are Hiding Your Data. Here’s a Simple Fix

How overlaying raw data points reveals what your summary statistics quietly discard

How to Organise Jupyter Notebooks So Older Work Stays Usable

A lightweight workflow for keeping notebook-heavy projects searchable, readable, and easy to return to

From Default to Publication-Ready: Transforming Matplotlib Histograms

Seven Steps From a Default Chart to Something Worth Publishing

When a Jupyter Notebook Is the Wrong Tool: Moving Logic Into Modules and Scripts

Notebooks are great for thinking. They are not always the right place to keep the machinery

Ridgeline Plots in Matplotlib: An Underused Way to Compare Distributions

Build stacked KDE plots from scratch and with joypy to show shape, skew, and spread across multiple groups

The Project Structure That Makes Good Jupyter Notebook Habits Automatic

Set it up once. Use it on every project.

6 Jupyter Notebook Habits That Make Your Work Easy to Return To

From messy exploration to a record that still makes sense months later

Why Whole-Well Completeness Numbers Can Hide Critical Data Gaps

Visualising Well Log Data Availability by Formation Across Multiple Wells

Before You Let AI Write Code, Define the Rules

Why missing specifications can result in behavioural drift in technical workflows

3 Python Charts You’re Probably Not Using (But Should Be)

Three underused chart types and when to reach for them when working with well log data

From Default to Publication-Ready: Transforming Matplotlib Heatmaps in 8 Steps

Take a Basic Well Log Data Availability Heatmap and Make It Something Worth Putting in a Report

AI That Suggests vs AI That Acts

Distinguishing between AI systems that suggest versus those that act autonomously is more critical than accuracy metrics alone.

Visualising Well Log Data Availability in the Terminal with Python

Building a coverage heatmap for LAS files using rich and lasio

The Jagged Frontier: Why AI Amazes and Frustrates in Equal Measure

Understanding where AI excels and where it falls apart is the skill that actually matters

Inspecting LAS Files in the Terminal with rich and lasio

A practical approach to inspecting LAS files in Python without jumping straight to plots

Working With LAS Files in Python

A practical look at the LAS format, its structure, and how to work with it using lasio for subsurface well data

Working With Agentic LLMs: Why Structured Workflows Matter More Than Clever Prompts

A geoscientist’s approach to controlled AI-assisted coding

AI in Petrophysics: When Did Everything Become Artificial Intelligence?

I've been thinking a lot about how casually we use the word AI at the moment.